859 件 ・ 関連度順
概要と使いどころ
Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.
日本語の概要は準備中です。原文の説明を表示しています。
Use when generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a `source-sample.json` in the exact shape the `upload-ad-sample` skill writes to the Goose Ads library. Also links every character and voice to the central character library at `<repo-root>/assets/character-library/` (repo-root derived from the run-dir, not a hardcoded path), and if a character isn't in the library yet, adds it first then links. Use when the user wants to remix one of their existing ads — this skill produces the source JSON that the script-rewriting step and `remix-ad` consume.
日本語の概要は準備中です。原文の説明を表示しています。
Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.
日本語の概要は準備中です。原文の説明を表示しています。
Populates Dataverse tables with sample records for testing and demoing a Power Pages site. Use when the user wants to add sample data, seed data, generate test records, or insert demo data into their tables.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to seed Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order. Mirrors microsoft/power-platform-skills/power-pages/add-sample-data, adapted for mobile apps.
日本語の概要は準備中です。原文の説明を表示しています。
A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
日本語の概要は準備中です。原文の説明を表示しています。
Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.
日本語の概要は準備中です。原文の説明を表示しています。
Downsamples, resizes, and compresses images using Volcengine LAS with multiple interpolation methods (lanczos, bilinear, nearest). Image resizing, image scaling, and image compression — reduces image resolution, dimensions, and file size. Changes and adjusts DPI settings. Converts between image formats including JPEG, PNG, TIFF. Generates thumbnails and optimizes images for web or print. Supports batch resize for photos and images up to 100MB and 225 megapixels. Note: downscale only, does not upscale. Use this skill when the user wants to resize or downscale images, compress images to reduce file size, change image format (JPEG/PNG/TIFF), adjust image DPI, generate image thumbnails, optimize images for web/print, or batch process multiple images.
日本語の概要は準備中です。原文の説明を表示しています。
Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.
日本語の概要は準備中です。原文の説明を表示しています。
Guides privacy audit sampling methodology including statistical and non-statistical sampling, sample size determination, stratification techniques, attribute sampling for compliance testing, confidence level selection, tolerable deviation rates, and extrapolation of results to the population. Keywords: audit sampling, statistical sampling, attribute testing, sample size, confidence level, stratified sampling, privacy audit.
日本語の概要は準備中です。原文の説明を表示しています。
Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.
日本語の概要は準備中です。原文の説明を表示しています。
Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.
日本語の概要は準備中です。原文の説明を表示しています。
CesiumJS time, properties, and animation - Clock, JulianDate, TimeInterval, Property, SampledProperty, CallbackProperty, PathMode, interval and sampled path materials, interpolation, splines, CZML temporal data. Use when making entity attributes or path materials time-dynamic, configuring the simulation clock, interpolating positions, or working with sampled, interval, or callback properties.
日本語の概要は準備中です。原文の説明を表示しています。
Plan a comprehensive spectroscopic analysis campaign by defining the analytical question, assessing sample characteristics, selecting appropriate techniques using a decision matrix, planning sample preparation for each technique, sequencing analyses from non-destructive to destructive, and defining success criteria with a cross-validation strategy.
日本語の概要は準備中です。原文の説明を表示しています。
Uses YARA to classify and triage samples at scale: applying rule sets, reading matches and string offsets, tuning for false positives, and organizing rules for malware family identification. Activates for requests to scan files with YARA, apply YARA rules, or classify samples by signature.
日本語の概要は準備中です。原文の説明を表示しています。
Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. Activates for requests to diff two samples, compare malware variants, or measure similarity between two binaries for variant tracking.
日本語の概要は準備中です。原文の説明を表示しています。
Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.
日本語の概要は準備中です。原文の説明を表示しています。
Integrates Sentry error tracking and performance monitoring into Flutter apps using `sentry_flutter`. Use when setting up crash reporting in Flutter, calling `SentryFlutter.init`, adding `SentryNavigatorObserver` to GoRouter, tracing HTTP requests with `sentry_dio`, capturing handled exceptions via `Sentry.captureException`, adding breadcrumbs, configuring session replay (`replay.onErrorSampleRate`), tuning `tracesSampleRate` or `profilesSampleRate`, controlling PII with `sendDefaultPii`, or setting user context after login and logout.
日本語の概要は準備中です。原文の説明を表示しています。
Minimal always-on sample skill to guarantee an end-to-end review experience.
日本語の概要は準備中です。原文の説明を表示しています。
Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.
日本語の概要は準備中です。原文の説明を表示しています。